# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Run masked LM/next sentence masked_lm pre-training for BERT."""

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import modeling
import optimization
import tensorflow as tf
from mylogger import logger

os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

flags = tf.flags

FLAGS = flags.FLAGS

## Required parameters
flags.DEFINE_string("bert_config_file", "uncased_L-12_H-768_A-12/bert_config.json", "定义网络结构的配置文件")

flags.DEFINE_string("input_file", "tmp/tf_examples.tfrecord",
                    "Input TF example files (can be a glob or comma separated).")

flags.DEFINE_string("output_dir", "tmp/checkpoint", "pre train 之后的model保存位置")

## Other parameters
flags.DEFINE_string("init_checkpoint", "uncased_L-12_H-768_A-12", "restore前面已经训练模型的保存位置")
# flags.DEFINE_string("init_checkpoint", "uncased_L-12_H-768_A-12\\bert_model", "restore前面已经训练模型的保存位置")
# flags.DEFINE_string("init_checkpoint", None, "restore前面已经训练模型的保存位置")

# flags.DEFINE_integer("max_seq_length", 128,
flags.DEFINE_integer("max_seq_length", 64,
                     "The maximum total input sequence length after WordPiece tokenization. "
                     "Sequences longer than this will be truncated, and sequences shorter "
                     "than this will be padded. Must match data generation.")

flags.DEFINE_integer("max_predictions_per_seq", 20,
                     "Maximum number of masked LM predictions per sequence. "
                     "Must match data generation.")

flags.DEFINE_bool("do_train", True, "是否训练模型")

flags.DEFINE_bool("do_eval", False, "是否对训练结果进行评估")

flags.DEFINE_integer("train_batch_size", 32, "Total batch size for training.")

flags.DEFINE_integer("eval_batch_size", 8, "Total batch size for eval.")

# 训练手learning rate 定义
flags.DEFINE_float("learning_rate", 5e-5, "The initial learning rate for Adam.")
flags.DEFINE_integer("num_train_steps", 100000, "Number of training steps.")
flags.DEFINE_integer("num_warmup_steps", 10000, "Number of warmup steps.")

flags.DEFINE_integer("save_checkpoints_steps", 1000, "How often to save the model checkpoint.")
# flags.DEFINE_integer("save_checkpoints_steps", 1, "How often to save the model checkpoint.")

flags.DEFINE_integer("iterations_per_loop", 1000, "How many steps to make in each estimator call.")

flags.DEFINE_integer("max_eval_steps", 100, "Maximum number of eval steps.")

# 训练是否使用谷歌的TPU
flags.DEFINE_bool("use_tpu", False, "Whether to use TPU or GPU/CPU.")
tf.flags.DEFINE_string(
    "tpu_name", None,
    "The Cloud TPU to use for training. This should be either the name "
    "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 "
    "url.")
tf.flags.DEFINE_string(
    "tpu_zone", None,
    "[Optional] GCE zone where the Cloud TPU is located in. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")
tf.flags.DEFINE_string(
    "gcp_project", None,
    "[Optional] Project name for the Cloud TPU-enabled project. If not "
    "specified, we will attempt to automatically detect the GCE project from "
    "metadata.")
tf.flags.DEFINE_string("master", None, "[Optional] TensorFlow master URL.")
flags.DEFINE_integer(
    "num_tpu_cores", 8,
    "Only used if `use_tpu` is True. Total number of TPU cores to use.")


def model_fn_builder(bert_config,
                     init_checkpoint,
                     learning_rate,
                     num_train_steps,
                     num_warmup_steps,
                     use_tpu,
                     use_one_hot_embeddings
                     ):
    """
    Returns `model_fn` closure for TPUEstimator.
    :param bert_config:  模型的archive文件
    :param init_checkpoint:
    :param learning_rate:
    :param num_train_steps:
    :param num_warmup_steps:
    :param use_tpu:
    :param use_one_hot_embeddings:
    :return:
    """

    def model_fn(features, labels, mode, params):  # pylint: disable=unused-argument
        # 传进paras的是什么
    # def model_fn(features, mode):  # pylint: disable=unused-argument
        """The `model_fn` for TPUEstimator."""

        logger.info("*** Features 函数的训练样本特征***")
        for name in sorted(features.keys()):
            logger.info("  name = %s, shape = %s" % (name, features[name].shape))

        input_ids = features["input_ids"]  # mask之后的句子句子的序列化
        input_mask = features["input_mask"]
        segment_ids = features["segment_ids"]  # A B 两个序列
        masked_lm_positions = features["masked_lm_positions"]  # mask position
        masked_lm_ids = features["masked_lm_ids"]  # mask 序列胡
        masked_lm_weights = features["masked_lm_weights"]  # 被mask的权重
        next_sentence_labels = features["next_sentence_labels"]  # 是否是下一句

        # ???
        is_training = (mode == tf.estimator.ModeKeys.TRAIN)
        logger.info(is_training)

        model = modeling.BertModel(
            config=bert_config,
            is_training=is_training,
            input_ids=input_ids,
            input_mask=input_mask,
            token_type_ids=segment_ids,
            use_one_hot_embeddings=use_one_hot_embeddings)

        masked_lm_loss, masked_lm_example_loss, masked_lm_log_probs = get_masked_lm_output(
            bert_config,
            model.get_sequence_output(),
            model.get_embedding_table(),
            masked_lm_positions,
            masked_lm_ids,
            masked_lm_weights
        )

        next_sentence_loss, next_sentence_example_loss, next_sentence_log_probs = get_next_sentence_output(
            bert_config,
            model.get_pooled_output(),
            next_sentence_labels
        )

        total_loss = masked_lm_loss + next_sentence_loss

        tvars = tf.trainable_variables()

        initialized_variable_names = {}
        scaffold_fn = None
        if init_checkpoint:
            assignment_map, initialized_variable_names = modeling.get_assignment_map_from_checkpoint(tvars, init_checkpoint)
            if use_tpu:

                def tpu_scaffold():
                    tf.train.init_from_checkpoint(init_checkpoint, assignment_map)
                    return tf.train.Scaffold()

                scaffold_fn = tpu_scaffold
            else:
                tf.train.init_from_checkpoint(init_checkpoint, assignment_map)

        logger.info("**** Trainable Variables ****")
        for var in tvars:
            init_string = ""
            if var.name in initialized_variable_names:
                init_string = ", *INIT_FROM_CKPT*"
            logger.info("  name = %s, shape = %s%s", var.name, var.shape,
                        init_string)

        output_spec = None
        if mode == tf.estimator.ModeKeys.TRAIN:
            train_op = optimization.create_optimizer(
                total_loss, learning_rate, num_train_steps, num_warmup_steps, use_tpu)

            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                train_op=train_op,
                scaffold_fn=scaffold_fn)
        elif mode == tf.estimator.ModeKeys.EVAL:

            def metric_fn(masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
                          masked_lm_weights, next_sentence_example_loss,
                          next_sentence_log_probs, next_sentence_labels):
                """Computes the loss and accuracy of the model."""
                masked_lm_log_probs = tf.reshape(masked_lm_log_probs,
                                                 [-1, masked_lm_log_probs.shape[-1]])
                masked_lm_predictions = tf.argmax(masked_lm_log_probs, axis=-1, output_type=tf.int32)
                masked_lm_example_loss = tf.reshape(masked_lm_example_loss, [-1])
                masked_lm_ids = tf.reshape(masked_lm_ids, [-1])
                masked_lm_weights = tf.reshape(masked_lm_weights, [-1])

                masked_lm_accuracy = tf.metrics.accuracy(
                    labels=masked_lm_ids,
                    predictions=masked_lm_predictions,
                    weights=masked_lm_weights
                )
                masked_lm_mean_loss = tf.metrics.mean(values=masked_lm_example_loss, weights=masked_lm_weights)

                next_sentence_log_probs = tf.reshape(next_sentence_log_probs, [-1, next_sentence_log_probs.shape[-1]])
                next_sentence_predictions = tf.argmax(next_sentence_log_probs, axis=-1, output_type=tf.int32)
                next_sentence_labels = tf.reshape(next_sentence_labels, [-1])
                next_sentence_accuracy = tf.metrics.accuracy(labels=next_sentence_labels,
                                                             predictions=next_sentence_predictions)
                next_sentence_mean_loss = tf.metrics.mean(values=next_sentence_example_loss)

                return {
                    # 返回
                    "masked_lm_accuracy": masked_lm_accuracy,
                    "masked_lm_loss": masked_lm_mean_loss,
                    "next_sentence_accuracy": next_sentence_accuracy,
                    "next_sentence_loss": next_sentence_mean_loss,
                }

            eval_metrics = (metric_fn, [
                masked_lm_example_loss, masked_lm_log_probs, masked_lm_ids,
                masked_lm_weights, next_sentence_example_loss,
                next_sentence_log_probs, next_sentence_labels
            ])
            output_spec = tf.contrib.tpu.TPUEstimatorSpec(
                mode=mode,
                loss=total_loss,
                eval_metrics=eval_metrics,
                scaffold_fn=scaffold_fn)
        else:
            raise ValueError("Only TRAIN and EVAL modes are supported: %s" % (mode))

        return output_spec

    return model_fn


def get_masked_lm_output(bert_config, input_tensor, output_weights, positions,
                         label_ids, label_weights):
    """Get loss and log probs for the masked LM."""
    input_tensor = gather_indexes(input_tensor, positions)

    with tf.variable_scope("cls/predictions"):
        # We apply one more non-linear transformation before the output layer.
        # This matrix is not used after pre-training.
        with tf.variable_scope("transform"):
            input_tensor = tf.layers.dense(
                input_tensor,
                units=bert_config.hidden_size,
                activation=modeling.get_activation(bert_config.hidden_act),
                kernel_initializer=modeling.create_initializer(
                    bert_config.initializer_range))
            input_tensor = modeling.layer_norm(input_tensor)

        # The output weights are the same as the input embeddings, but there is
        # an output-only bias for each token.
        output_bias = tf.get_variable(
            "output_bias",
            shape=[bert_config.vocab_size],
            initializer=tf.zeros_initializer())
        logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        log_probs = tf.nn.log_softmax(logits, axis=-1)

        label_ids = tf.reshape(label_ids, [-1])
        label_weights = tf.reshape(label_weights, [-1])

        one_hot_labels = tf.one_hot(
            label_ids, depth=bert_config.vocab_size, dtype=tf.float32)

        # The `positions` tensor might be zero-padded (if the sequence is too
        # short to have the maximum number of predictions). The `label_weights`
        # tensor has a value of 1.0 for every real prediction and 0.0 for the
        # padding predictions.
        per_example_loss = -tf.reduce_sum(log_probs * one_hot_labels, axis=[-1])
        numerator = tf.reduce_sum(label_weights * per_example_loss)
        denominator = tf.reduce_sum(label_weights) + 1e-5
        loss = numerator / denominator

    return (loss, per_example_loss, log_probs)


def get_next_sentence_output(bert_config, input_tensor, labels):
    """Get loss and log probs for the next sentence prediction."""

    # Simple binary classification. Note that 0 is "next sentence" and 1 is
    # "random sentence". This weight matrix is not used after pre-training.
    with tf.variable_scope("cls/seq_relationship"):
        output_weights = tf.get_variable(
            "output_weights",
            shape=[2, bert_config.hidden_size],
            initializer=modeling.create_initializer(bert_config.initializer_range))
        output_bias = tf.get_variable(
            "output_bias", shape=[2], initializer=tf.zeros_initializer())

        logits = tf.matmul(input_tensor, output_weights, transpose_b=True)
        logits = tf.nn.bias_add(logits, output_bias)
        log_probs = tf.nn.log_softmax(logits, axis=-1)
        labels = tf.reshape(labels, [-1])
        one_hot_labels = tf.one_hot(labels, depth=2, dtype=tf.float32)
        per_example_loss = -tf.reduce_sum(one_hot_labels * log_probs, axis=-1)
        loss = tf.reduce_mean(per_example_loss)
        return (loss, per_example_loss, log_probs)


def gather_indexes(sequence_tensor, positions):
    """Gathers the vectors at the specific positions over a minibatch."""
    sequence_shape = modeling.get_shape_list(sequence_tensor, expected_rank=3)
    batch_size = sequence_shape[0]
    seq_length = sequence_shape[1]
    width = sequence_shape[2]

    flat_offsets = tf.reshape(
        tf.range(0, batch_size, dtype=tf.int32) * seq_length, [-1, 1])
    flat_positions = tf.reshape(positions + flat_offsets, [-1])
    flat_sequence_tensor = tf.reshape(sequence_tensor,
                                      [batch_size * seq_length, width])
    output_tensor = tf.gather(flat_sequence_tensor, flat_positions)
    return output_tensor


def input_fn_builder(input_files,
                     max_seq_length,
                     max_predictions_per_seq,
                     is_training,
                     num_cpu_threads=4):
    """Creates an `input_fn` closure to be passed to TPUEstimator."""

    def input_fn(params):
        """The actual input function."""
        batch_size = params["batch_size"]

        name_to_features = {
            "input_ids": tf.FixedLenFeature(shape=[max_seq_length], dtype=tf.int64),
            "input_mask": tf.FixedLenFeature([max_seq_length], tf.int64),
            "segment_ids": tf.FixedLenFeature([max_seq_length], tf.int64),
            "masked_lm_positions": tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
            "masked_lm_ids": tf.FixedLenFeature([max_predictions_per_seq], tf.int64),
            "masked_lm_weights": tf.FixedLenFeature([max_predictions_per_seq], tf.float32),
            "next_sentence_labels": tf.FixedLenFeature([1], tf.int64),
        }

        # For training, we want a lot of parallel reading and shuffling.
        # For eval, we want no shuffling and parallel reading doesn't matter.
        if is_training:
            d = tf.data.Dataset.from_tensor_slices(tf.constant(input_files))
            d = d.repeat()
            d = d.shuffle(buffer_size=len(input_files))

            # `cycle_length` is the number of parallel files that get read.
            cycle_length = min(num_cpu_threads, len(input_files))

            # `sloppy` mode means that the interleaving is not exact. This adds
            # even more randomness to the training pipeline.
            d = d.apply(
                tf.contrib.data.parallel_interleave(
                    tf.data.TFRecordDataset,
                    sloppy=is_training,
                    cycle_length=cycle_length
                )
            )
            d = d.shuffle(buffer_size=100)
        else:
            d = tf.data.TFRecordDataset(input_files)
            # Since we evaluate for a fixed number of steps we don't want to encounter
            # out-of-range exceptions.
            d = d.repeat()

        # We must `drop_remainder` on training because the TPU requires fixed
        # size dimensions. For eval, we assume we are evaluating on the CPU or GPU
        # and we *don't* want to drop the remainder, otherwise we wont cover
        # every sample.
        d = d.apply(
            tf.contrib.data.map_and_batch(
                lambda record: _decode_record(record, name_to_features),
                batch_size=batch_size,
                num_parallel_batches=num_cpu_threads,
                drop_remainder=True))
        return d

    return input_fn


def _decode_record(record, name_to_features):
    """Decodes a record to a TensorFlow example."""
    example = tf.parse_single_example(record, name_to_features)

    # tf.Example only supports tf.int64, but the TPU only supports tf.int32.
    # So cast all int64 to int32.
    for name in list(example.keys()):
        t = example[name]
        if t.dtype == tf.int64:
            t = tf.to_int32(t)
        example[name] = t

    return example


def main(_):
    tf.logging.set_verbosity(tf.logging.INFO)

    if not FLAGS.do_train and not FLAGS.do_eval:
        raise ValueError("At least one of `do_train` or `do_eval` must be True.")

    bert_config = modeling.BertConfig.from_json_file(FLAGS.bert_config_file)

    # 模型保存位置
    tf.gfile.MakeDirs(FLAGS.output_dir)

    input_files = []
    for input_pattern in FLAGS.input_file.split(","):
        input_files.extend(tf.gfile.Glob(input_pattern))

    logger.info("*** Input Files ***")
    for input_file in input_files:
        logger.info("  %s" % input_file)

    tpu_cluster_resolver = None
    if FLAGS.use_tpu and FLAGS.tpu_name:
        # 这里并没有tpu可用
        tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(FLAGS.tpu_name, zone=FLAGS.tpu_zone,
                                                                              project=FLAGS.gcp_project)

    is_per_host = tf.contrib.tpu.InputPipelineConfig.PER_HOST_V2
    logger.info(is_per_host)
    # tpu 运行时候阐述
    tpu_config = tf.contrib.tpu.TPUConfig(iterations_per_loop=FLAGS.iterations_per_loop,
                                          num_shards=FLAGS.num_tpu_cores,
                                          per_host_input_for_training=is_per_host
                                          )
    logger.info(tpu_config)
    # TPUConfig(iterations_per_loop=1000, num_shards=8, num_cores_per_replica=None, per_host_input_for_training=3,
    #           tpu_job_name=None, initial_infeed_sleep_secs=None, input_partition_dims=None)

    # 运行参数
    run_config = tf.contrib.tpu.RunConfig(
        cluster=tpu_cluster_resolver,
        master=FLAGS.master,
        model_dir=FLAGS.output_dir,
        save_checkpoints_steps=FLAGS.save_checkpoints_steps,
        tpu_config=tpu_config
    )
    logger.info(run_config)

    # 在这里并没有初始化模型，仅仅是返回一个构建模型的函数体
    model_fn = model_fn_builder(
        bert_config=bert_config,
        init_checkpoint=FLAGS.init_checkpoint,
        learning_rate=FLAGS.learning_rate,
        num_train_steps=FLAGS.num_train_steps,
        num_warmup_steps=FLAGS.num_warmup_steps,
        use_tpu=FLAGS.use_tpu,
        use_one_hot_embeddings=FLAGS.use_tpu
    )

    # If TPU is not available, this will fall back to normal Estimator on CPU or GPU.
    logger.info("whether use tpu: %s" % str(FLAGS.use_tpu))
    estimator = tf.contrib.tpu.TPUEstimator(
        use_tpu=FLAGS.use_tpu,
        model_fn=model_fn,  # 模型函数
        config=run_config,
        train_batch_size=FLAGS.train_batch_size,
        eval_batch_size=FLAGS.eval_batch_size
    )

    logger.info(input_files)
    if FLAGS.do_train:
        logger.info("***** Running training *****")
        logger.info("  Batch size = %d", FLAGS.train_batch_size)
        # 训练的输出函数
        train_input_fn = input_fn_builder(
            input_files=input_files,
            max_seq_length=FLAGS.max_seq_length,
            max_predictions_per_seq=FLAGS.max_predictions_per_seq,
            is_training=True
        )
        # 利用训练数据训练模型
        estimator.train(input_fn=train_input_fn, max_steps=FLAGS.num_train_steps)

    if FLAGS.do_eval:
        logger.info("***** Running evaluation *****")
        logger.info("  Batch size = %d", FLAGS.eval_batch_size)

        eval_input_fn = input_fn_builder(
            input_files=input_files,
            max_seq_length=FLAGS.max_seq_length,
            max_predictions_per_seq=FLAGS.max_predictions_per_seq,
            is_training=False
        )

        # 自带evaluate函数
        result = estimator.evaluate(input_fn=eval_input_fn, steps=FLAGS.max_eval_steps)

        output_eval_file = os.path.join(FLAGS.output_dir, "eval_results.txt")
        with tf.gfile.GFile(output_eval_file, "w") as writer:
            logger.info("***** Eval results *****")
            for key in sorted(result.keys()):
                logger.info("  %s = %s", key, str(result[key]))
                writer.write("%s = %s\n" % (key, str(result[key])))


if __name__ == "__main__":
    flags.mark_flag_as_required("input_file")
    flags.mark_flag_as_required("bert_config_file")
    flags.mark_flag_as_required("output_dir")
    tf.app.run()
